Thursday, February 4, 2021

How Might AI affect Trusted Advisor Businesses?

Information disparity explains why enterprises and smaller businesses buying information technology use consultants and system integrators. IT decisions often are complex, almost always lie outside the buyer’s domain expertise and can be significant choices with long-term consequences. 


Some 64 percent of 500 respondents to an Avant Communications poll of U.S. based enterprise decision makers use third party consultants, for example. 


source: Avant Communications 


The role of the advisors is to reduce buyer risk by providing expert advice, and therefore is an “information” service. At a high level, that raises the question of what happens--eventually--as artificial intelligence advances.


source: Avant Communications 


Predictably, experts in fields ranging from finance to law to  management consulting argue that AI cannot replace human judgment and insight. Others point to growing AI roles and argue AI will replace trusted advisors. To be sure, most expect AI displacement for any jobs with routine tasks.


The issue is that AI will become more capable over time, allowing AI systems to potentially displace many more functions at a higher level, even when those human-supplied functions involve “advice.” 


Consider the advice consultants can provide. Much of that advice concerns the choices buyers should be making to place advertising, buy one technology over another, evaluate the life cycle costs of such choices or use cloud computing in place of premises computing on owned hardware, for example. 


In the connectivity business, many of the buyer choices involve MPLS, SD-WAN, cloud computing, security or unified communications. But the value of AI is, in part, the ability to automate any rule-based systems. And the choices between MPLS and SD-WAN are largely rule-based, in terms of total cost of ownership. 


Increasingly, the total cost of ownership choices between cloud computing and do-it-yourself computing are understood and can be routinized. That does not mean complete replacement of trusted advisors, but arguably moves the value of those advisors “up the complexity stack” to areas where benefits and costs are more intangible. 


To the extent that information disparity creates the value added by third party trusted advisors, AI is going to erode the extent of that information disadvantage, and therefore alter the places where advisors can add value. 


So AI will start to replace the lower-order information mismatches at first, then gradually begin to replace more-complex mismatches over time. McKinsey studies suggest AI will replace parts of tasks, perhaps 30 percent of job tasks by about 2030. That might not fully replace jobs, but will change them. 


But most expect the changes to deepen over time, as AI becomes more capable, and can handle more complex information-related tasks. The shift of U.S. jobs from farming to other pursuits did not happen immediately, either, and was not directly affected by information technology as by mechanical technology. But it happened. 


Some 90 percent of U.S. residents once worked on farms. Today about one percent do so. Digital transformation, defined as the use of digital technologies to create new--or modify existing--business processes, culture, and customer experiences to meet changing business and market requirements, has been going on since the early 1980s, for example. 


Few students today realize that, in 1977, nobody used a personal computer at school. Nobody used the internet or a mobile phone. In 1984 perhaps eight percent of U.S. homes had a computer.  


Businesses did not routinely begin using PCs until after IBM introduced its first IBM PC in 1981. 


The point is that AI, as have other forms of digital transformation, starts slow. But transformation does eventually happen. People do not work, learn or live the same way. Eventually, a wide range of tasks now performed by trusted advisors will be handled by AI-based apps and services. 


“Advice” will be among those tasks. The only issue is how much advice can be provided by algorithmic systems.


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Personalization and Privacy are Tradeoffs

Personsalization necessarily involves giving up some privacy. So lots of personalization means giving up lots of privacy.